Computing Minimum Description Length for Robust Linear Regression Model Selection
Computing Minimum Description Length for Robust Linear Regression Model Selection
复制标题
计算鲁棒线性回归模型选择的最小描述长度
DOI：
10.1142/9789814447300_0031
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发表时间：
1998
期刊：
影响因子：
--
通讯作者：
G. Qian
中科院分区：
文献类型：
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作者：
G. Qian
研究了稳健线性回归模型选择的最小描述长度（MDL）和随机复杂度方法。计算方面和实现这种方法的实际问题是研究的重点。特别是，我们提供了算法和S语言程序包计算的随机复杂性和进行相关的模型选择。仿真研究，然后提出的说明和比较MDL方法与常用的AIC和BIC方法。最后，应用于铁人三项运动员的生理研究。
A minimum description length (MDL) and stochastic complexity approach for model selection in robust linear regression is studied in this paper. Computational aspects and implementation of this approach to practical problems are the focuses of the study. Particularly, we provide both algorithms and a package of S language programs for computing the stochastic complexity and proceeding with the associated model selection. A simulation study is then presented for illustration and comparing the MDL approach with the commonly used AIC and BIC methods. Finally, an application is given to a physiological study of triathlon athletes.